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High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR

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      Crop improvement efforts are targeting increased above-ground biomass and radiation-use efficiency as drivers for greater yield. Early ground cover and canopy height contribute to biomass production, but manual measurements of these traits, and in particular above-ground biomass, are slow and labor-intensive, more so when made at multiple developmental stages. These constraints limit the ability to capture these data in a temporal fashion, hampering insights that could be gained from multi-dimensional data. Here we demonstrate the capacity of Light Detection and Ranging (LiDAR), mounted on a lightweight, mobile, ground-based platform, for rapid multi-temporal and non-destructive estimation of canopy height, ground cover and above-ground biomass. Field validation of LiDAR measurements is presented. For canopy height, strong relationships with LiDAR ( r 2 of 0.99 and root mean square error of 0.017 m) were obtained. Ground cover was estimated from LiDAR using two methodologies: red reflectance image and canopy height. In contrast to NDVI, LiDAR was not affected by saturation at high ground cover, and the comparison of both LiDAR methodologies showed strong association ( r 2 = 0.92 and slope = 1.02) at ground cover above 0.8. For above-ground biomass, a dedicated field experiment was performed with destructive biomass sampled eight times across different developmental stages. Two methodologies are presented for the estimation of biomass from LiDAR: 3D voxel index (3DVI) and 3D profile index (3DPI). The parameters involved in the calculation of 3DVI and 3DPI were optimized for each sample event from tillering to maturity, as well as generalized for any developmental stage. Individual sample point predictions were strong while predictions across all eight sample events, provided the strongest association with biomass ( r 2 = 0.93 and r 2 = 0.92) for 3DPI and 3DVI, respectively. Given these results, we believe that application of this system will provide new opportunities to deliver improved genotypes and agronomic interventions via more efficient and reliable phenotyping of these important traits in large experiments.

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          Phenomics--technologies to relieve the phenotyping bottleneck.

          Global agriculture is facing major challenges to ensure global food security, such as the need to breed high-yielding crops adapted to future climates and the identification of dedicated feedstock crops for biofuel production (biofuel feedstocks). Plant phenomics offers a suite of new technologies to accelerate progress in understanding gene function and environmental responses. This will enable breeders to develop new agricultural germplasm to support future agricultural production. In this review we present plant physiology in an 'omics' perspective, review some of the new high-throughput and high-resolution phenotyping tools and discuss their application to plant biology, functional genomics and crop breeding. Crown Copyright © 2011. Published by Elsevier Ltd. All rights reserved.

            Author and article information

            1High Resolution Plant Phenomics Centre, Commonwealth Scientific and Industrial Research Organisation Agriculture and Food Agriculture and Food , Canberra, ACT, Australia
            2Commonwealth Scientific and Industrial Research Organisation Agriculture and Food , Canberra, ACT, Australia
            3ARC Centre of Excellence for Translational Photosynthesis, Australian National University , Canberra, ACT, Australia
            Author notes

            Edited by: Yann Guédon, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), France

            Reviewed by: Helge Aasen, ETH Zurich, Switzerland; Nicolas Virlet, Rothamsted Research, United Kingdom

            This article was submitted to Technical Advances in Plant Science, a section of the journal Frontiers in Plant Science

            †Present Address: Jose A. Jimenez-Berni, Instituto Agricultura Sostenible, Consejo Superior de Investigaciones Cientificas, Cordoba, Spain

            Pablo Rozas-Larraondo, National Computational Infrastructure, Australian National University, Acton, ACT, Australia

            Front Plant Sci
            Front Plant Sci
            Front. Plant Sci.
            Frontiers in Plant Science
            Frontiers Media S.A.
            27 February 2018
            : 9
            5835033 10.3389/fpls.2018.00237
            Copyright © 2018 Jimenez-Berni, Deery, Rozas-Larraondo, Condon, Rebetzke, James, Bovill, Furbank and Sirault.

            This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

            Figures: 12, Tables: 2, Equations: 7, References: 72, Pages: 18, Words: 12206
            Funded by: Grains Research and Development Corporation 10.13039/501100000980
            Plant Science

            Plant science & Botany

            lidar, plant phenomics, above-ground biomass, ndvi, field experiments


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